• DocumentCode
    1435079
  • Title

    Possibilistic-Scenario Model for DG Impact Assessment on Distribution Networks in an Uncertain Environment

  • Author

    Soroudi, Alireza

  • Author_Institution
    Sci. & Res. Branch, Islamic Azad Univ., Tehran, Iran
  • Volume
    27
  • Issue
    3
  • fYear
    2012
  • Firstpage
    1283
  • Lastpage
    1293
  • Abstract
    The distribution network operators (DNOs) are responsible for securing a diverse and viable energy supply for their customers so the technical and economical impacts of distributed generation (DG) units are of great concerns. Traditionally, the DNOs try to maximize the technical performance of the distribution network, but it is evident that the first step in optimizing a quantity is being able to calculate it. The DG investment/operation which is performed by distributed generation operators/owners (DGOs) (under unbundling rules) has made this task more complicated. This is mainly because the DNO is faced with the uncertainties related to the decisions of DG investors/operators where some of them can be probabilistically modeled while the others are possibilistically treated. This paper proposes a hybrid possibilistic-probabilistic DG impact assessment tool which takes into account the uncertainties associated with investment and operation of renewable and conventional DG units on distribution networks. This tool would be useful for DNOs to deal with the uncertainties which some of them can be modeled probabilistically and some of them are described possibilistically. The proposed method has been tested on a test system and a large-scale real distribution network to demonstrate its strength and flexibility.
  • Keywords
    distributed power generation; distribution networks; power system security; power system simulation; distributed generation impact assessment; distributed generation operators/owners; distributed generation units; distribution network operators; distribution networks; energy supply; possibilistic-scenario model; Investments; Load modeling; Probabilistic logic; Probability density function; Uncertainty; Wind speed; Wind turbines; Distributed generation (DG); fuzzy sets; risk analysis; stochastic approximation; uncertainty; wind energy;
  • fLanguage
    English
  • Journal_Title
    Power Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8950
  • Type

    jour

  • DOI
    10.1109/TPWRS.2011.2180933
  • Filename
    6142135